Volume 29 Issue 6
Dec.  2020
Turn off MathJax
Article Contents
LYU Yanxia, LI Wenjie, WANG Yue, SUN Siqi, WANG Cuirong. RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection[J]. Chinese Journal of Electronics, 2020, 29(6): 1093-1101. doi: 10.1049/cje.2020.09.010
Citation: LYU Yanxia, LI Wenjie, WANG Yue, SUN Siqi, WANG Cuirong. RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection[J]. Chinese Journal of Electronics, 2020, 29(6): 1093-1101. doi: 10.1049/cje.2020.09.010

RMHSForest: Relative Mass and Half-Space Tree Based Forest for Anomaly Detection

doi: 10.1049/cje.2020.09.010
More Information
  • Corresponding author: LYU Yanxia (corresponding author) received the Ph.D. degree in the School of Computer Science and Engineering, Northeastern University, Shenyang, China, in 2020. She is currently a lecturer with the School of Computer and Communication Engineering, Northeastern University at Qinhuangdao. She is a member of the CCF and ACM. Her current research interests include data mining, sentiment analysis and recommendation system. She has authored or co-authored 14 technical papers in journals, such as the Tsinghua Science and Technology, Advanced Engineering Informatics, Neural Computing & Applications. (Email:lyx@neuq.edu.cn)
  • Received Date: 2019-07-15
  • Publish Date: 2020-12-25
  • Anomaly detection refers to identify the true anomalies from a given data set. We present an ensemble anomaly detection method called Relative mass and half-space tree based forest (RMHSForest), which detect anomalies, including global and local anomalies, based on relative mass estimation and halfspace tree. Different from density or distance based measure, RMHSForest utilizes a novel relative mass estimation to improve the detection of local anomaly. Meanwhile, half-space tree based on augmented mass can estimate a mass distribution efficiently without density or distance calculations or clustering. Our empirical results show that RMHSForest outperforms the current popular anomaly detection algorithms in terms of AUC and processing time in the test data sets.
  • loading
  • Y.L. Zhang, J. Zhou, W. Zheng, et al., "Distributed deep forest and its application to automatic detection of cash-out fraud", CoRR, arXiv:/1805.04234, 2018.
    Y.G. Qian, "Network traffic anomaly detection based on maximum entropy model", Chinese Journal of Electronics, Vol.16, No.3, pp.579-582, 2012.
    D.B. Yuan, H. Li, F. Wang, et al., "A GNSS acquisition method with the capability of spoofing detection and mitigation", Chinese Journal of Electronics, Vol.27, No.1, pp.213-222, 2018.
    J.L.P. Lima, D. Macêdo and C. Zanchettin, "Heartbeat anomaly Detection using adversarial oversampling", arXiv e-prints, 2019.
    K.M. Ting, G.T. Zhou and F.T. Liu, "Mass estimation", Machine Learning, Vol.90, No.1, pp.127-160, 2013.
    F.T. Liu, K.M. Ting and Z.H. Zhou, "Isolation forest", Proc. of IEEE International Conference on Data Mining, Washington, DC, USA, pp.413-422, 2008.
    S. Aryal, K.M. Ting, J.R. Wells, et al., "Improving iForest with relative mass", Lecture Notes in Computer Science, Springer, Cham, pp.510-521, 2014.
    Tongfeng Sun, Shifei Ding, Pin Li, et al., "Acomparative study of neural-network feature weighting", Artificial Intelligence Review, Vol.52, No.1, pp.469-493, 2019.
    N. Zhang, et al., "Multi-view RBM with posterior consistency and domain adaptation", Information Sciences, Vol.516, pp.142-157, 2020.
    J. Wen, H.J. Wang, J. Deng, et al., "Abnormal event detection based on deep learning", ACTA Electronica Sinica, Vol.48, No.02, pp.308-313, 2020.
    N. Chawla and W. Wang, "Outlier detection with autoencoder ensembles", SIAM International Conference on Data Mining, pp.90-98, 2017.
    Y. Zhao and M.K. Hryniewicki, "XGBOD:Improving supervised outlier detection with unsupervised representation learning", 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, 2018.
    K.M. Ting, Y. Zhu and Z.H. Zhou, "Isolation kernel and its effect on SVM", Proc. of the 24th ACM SIGKDD International Conference, 2018.
    T.B. Wang, F.B. Zhang and C.H. Xia, "Research on loophole with second distribution of real value detectors", Chinese Journal of Electronics, Vol.25, No.06, pp.155-164, 2016.
    C.L. Wen, F.N. Zhou, C.B. Wen, et al., "An extended multi-scale principal component analysis method and application in anomaly detection", Chinese Journal of Electronics, Vol.21, No.3, pp.471-476, 2012.
    S. Li, X.F. Zhou, H.B. Shi, et al., "Monitoring of multimode processes based on subspace decomposition", Industrial and Engineering Chemistry Research, Vol.54, No.15, pp.3855-3864, 2015.
    J. Zhang, H. Wang, "Detecting outlying subspaces for high-dimensional data:The new task, algorithms, and performance", Knowledge and Information Systems, Vol.10, No.3, pp.333-355, 2006.
    P. Kaur, M. Kumar and A. Bhandari, "A review of detection approaches for distributed denial of service attacks", Systems Science and Control Engineering, Vol.5, No.1, pp.301-320, 2017.
    M.M. Breunig, H.P. Kriegel and R.T. Ng, "LOF:identifying density-based local outliers", Proc. of ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, pp.93-104, 2000.
    H. Zhao, H. Liu, Z. Ding, et al., "Consensus regularized multi-view outlier detection", IEEE Transactions on Image Processing, Vol.27, No.1, Page 236, 2018.
    F.P. Guo and H.X. Hui, "Anomaly detection algorithm based on the local distance of density-based sampling data", Journal of Software, 2017.
    F.T. Liu, K.M. Ting and G.T. Zhou, "On detecting clustered anomalies using SCiForest", Proc. of European Conference on Machine Learning and Knowledge Discovery in Databases, Barcelona, Spain, pp.274-290, 2010.
    R.N. Calheiros, K. Ramamohanarao, R. Buyya, et al., "On the effectiveness of isolation-based anomaly detection in cloud data", Concurrency and Computation:Practice and Experience, DOI:10.1002/cpe.4169, 2017
    S. Hariri, M.C. Kind and R.J. Brunner, "Extended isolation forest", IEEE Transactions on Knowledge and Data Engineering, pp.1-12, 2019.
    R. Williamson, A. Smola, J. Shawe-Taylor, et al., "Support vector method for novelty detection", Proc. of International Conference on Neural Information Processing Systems, Denver, Colorado, USA, pp.582-588, 1999.
    D. Droghini, D. Ferretti, E. Principi, et al., "A combined oneclass SVM and template-matching approach for user-aided human fall detection by means of floor acoustic features", Computational Intelligence and Neuroscience, pp.1-13, 2017
    F.T. Liu, K.M. Ting and Z.H. Zhou, "Isolation-based anomaly detection", ACM Transactions on Knowledge Discovery from Data, Vol.6, No.1, pp.1-39, 2012.
    W. Budgaga, M. Malensek, S.L. Sangmi, P. Shrideep, "A framework for scalable real-time anomaly detection over voluminous, geospatial data streams", Concurrency and Computation:Practice and Experience, Vol.29, No.12, DOI:10.1002/cpe.4106, 2017.
    K. Yamanishi, J.I. Takeuchi, G. Williams, et al., "On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms", Data Mining and Knowledge Discovery, Vol.8, No.3, pp.275-300, 2000.
    H.A. Futuhul, H. Moh and S. Halimatus, "Handling outlier in two-ways table data:the robustness of row-column interaction model", Journal of Physics:Conference Series, Vol.1028, DOI:10.1088/1742-6596/1028/1/012222, 2018.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (181) PDF downloads(48) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return